[Matting] Add android demo for human matting

contrib/Matting/deploy/human_matting_android_demo/.gitignore

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+*.iml
+.gradle
+/local.properties
+/.idea/caches
+/.idea/libraries
+/.idea/modules.xml
+/.idea/workspace.xml
+/.idea/navEditor.xml
+/.idea/assetWizardSettings.xml
+.DS_Store
+/build
+/captures
+.externalNativeBuild

contrib/Matting/deploy/human_matting_android_demo/LICENSE

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contrib/Matting/deploy/human_matting_android_demo/README.md

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+# human_matting_android_demo
+基于[PaddleSeg](https://github.com/paddlepaddle/paddleseg/tree/develop)的[MODNet](https://github.com/PaddlePaddle/PaddleSeg/tree/develop/contrib/Matting)算法实现人像抠图（安卓版demo）。
+
+可以直接下载安装本示例工程中的[apk](./app-debug.apk)进行体验。
+
+## 1. 效果展示
+<div align="center">
+<img src="figures/human.jpg"  width="50%"> 
+
+原图
+</div>
+
+<div align="center">
+<img src="figures/bg.jpg"  width="50%" >
+
+新背景
+
+</div>
+
+在手机上进行人像抠图然后再替换背景：
+
+<div align="center">
+<img src="figures/demo.jpg"  width="50%" >
+</div>
+
+
+## 2. 安卓Demo使用说明
+
+### 2.1 要求
+* Android Studio 3.4；
+* Android手机；
+
+### 2.2 一键安装
+* git clone https://github.com/qianbin1989228/human_matting_android_demo.git ;
+* 打开Android Studio，在"Welcome to Android Studio"窗口点击"Open an existing Android Studio project"，在弹出的路径选择窗口中选择刚git下来的文件夹，然后点击右下角的"Open"按钮即可导入工程，构建工程的过程中会自动下载demo需要的Lite预测库；
+* 通过USB连接Android手机；
+* 载入工程后，点击菜单栏的Run->Run 'App'按钮，在弹出的"Select Deployment Target"窗口选择已经连接的Android设备，然后点击"OK"按钮；
+
+*注：此安卓demo基于[Paddle-Lite](https://paddlelite.paddlepaddle.org.cn/)开发，PaddleLite版本为2.8.0。*
+
+### 2.3 预测
+* 在人像抠图Demo中，默认会载入一张人像图像，并会在图像下方给出CPU的预测结果和预测时延；
+* 在人像抠图Demo中，你还可以通过右上角的"打开本地相册"和"打开摄像头拍照"按钮分别从相册或相机中加载测试图像然后进行预测推理；
+
+*注意：demo中拍照时照片会自动压缩，想测试拍照原图效果，可使用手机相机拍照后从相册中打开进行预测。*
+
+## 3. 二次开发
+可按需要更新预测库或模型进行二次开发，其中更新模型分为模型导出和模型转换两个步骤。
+
+### 3.1 更新预测库
+[Paddle-Lite官网](https://paddlelite.paddlepaddle.org.cn/)提供了预编译版本的安卓预测库，也可以参考官网自行编译。
+
+Paddle-Lite在安卓端的预测库主要包括三个文件：
+
+* PaddlePredictor.jar；
+* arm64-v8a/libpaddle_lite_jni.so；
+* armeabi-v7a/libpaddle_lite_jni.so；
+
+下面分别介绍两种方法：
+
+* 使用预编译版本的预测库，最新的预编译文件参考：[release](https://github.com/PaddlePaddle/Paddle-Lite/releases/)，此demo使用的[版本](https://paddlelite-demo.bj.bcebos.com/libs/android/paddle_lite_libs_v2_8_0.tar.gz)
+
+	解压上面文件，PaddlePredictor.jar位于：java/PaddlePredictor.jar；
+
+	arm64-v8a相关so位于：java/libs/arm64-v8a；
+
+	armeabi-v7a相关so位于：java/libs/armeabi-v7a；
+
+* 手动编译Paddle-Lite预测库
+开发环境的准备和编译方法参考：[Paddle-Lite源码编译](https://paddle-lite.readthedocs.io/zh/release-v2.8/source_compile/compile_env.html)。
+
+准备好上述文件，即可参考[java_api](https://paddle-lite.readthedocs.io/zh/release-v2.8/api_reference/java_api_doc.html)在安卓端进行推理。具体使用预测库的方法可参考[Paddle-Lite-Demo](https://github.com/PaddlePaddle/Paddle-Lite-Demo)中更新预测库部分的文档。
+
+### 3.2 模型导出
+此demo的人像抠图采用Backbone为HRNet_W18的MODNet模型，模型[训练教程](https://github.com/PaddlePaddle/PaddleSeg/tree/develop/contrib/Matting)请参考官网，官网提供了3种不同性能的Backone：MobileNetV2、ResNet50_vd和HRNet_W18。本安卓demo综合考虑精度和速度要求，采用了HRNet_W18作为Backone。可以直接从官网下载训练好的动态图模型进行算法验证。
+
+为了能够在安卓手机上进行推理，需要将动态图模型导出为静态图模型，导出时固定图像输入尺寸即可。
+
+首先git最新的[PaddleSeg](https://github.com/paddlepaddle/paddleseg/tree/develop)项目，然后cd进入到PaddleSeg/contrib/Matting目录。将下载下来的modnet-hrnet_w18.pdparams动态图模型文件（也可以自行训练得到）放置在当前文件夹（PaddleSeg/contrib/Matting）下面。然后修改配置文件 configs/modnet_mobilenetv2.yml(注意：虽然采用hrnet18模型，但是该模型依赖的配置文件modnet_hrnet_w18.yml本身依赖modnet_mobilenetv2.yml),修改其中的val_dataset字段如下：
+
+``` yml
+val_dataset:
+  type: MattingDataset
+  dataset_root: data/PPM-100
+  val_file: val.txt
+  transforms:
+    - type: LoadImages
+    - type: ResizeByShort
+      short_size: 256
+    - type: ResizeToIntMult
+      mult_int: 32
+    - type: Normalize
+  mode: val
+  get_trimap: False
+```
+上述修改中尤其注意short_size: 256这个字段，这个值直接决定我们最终的推理图像采用的尺寸大小。这个字段值设置太小会影响预测精度，设置太大会影响手机推理速度（甚至造成手机因性能问题无法完成推理而奔溃）。经过实际测试，对于hrnet18，该字段设置为256较好。
+
+完成配置文件修改后，采用下面的命令进行静态图导出：
+``` shell
+python export.py \
+    --config configs/modnet/modnet_hrnet_w18.yml \
+    --model_path modnet-hrnet_w18.pdparams \
+    --save_dir output
+```
+
+转换完成后在当前目录下会生成output文件夹，该文件夹中的文件即为转出来的静态图文件。
+
+### 3.3 模型转换
+
+#### 3.3.1 模型转换工具
+准备好PaddleSeg导出来的静态图模型和参数文件后，需要使用Paddle-Lite提供的opt对模型进行优化，并转换成Paddle-Lite支持的文件格式。
+
+首先安装PaddleLite：
+
+``` shell
+pip install paddlelite==2.8.0
+```
+
+然后使用下面的python脚本进行转换：
+
+``` python
+# 引用Paddlelite预测库
+from paddlelite.lite import *
+
+# 1. 创建opt实例
+opt=Opt()
+
+# 2. 指定静态模型路径 
+opt.set_model_file('./output/model.pdmodel')
+opt.set_param_file('./output/model.pdiparams')
+
+# 3. 指定转化类型： arm、x86、opencl、npu
+opt.set_valid_places("arm")
+# 4. 指定模型转化类型： naive_buffer、protobuf
+opt.set_model_type("naive_buffer")
+# 5. 输出模型地址
+opt.set_optimize_out("./output/hrnet_w18")
+# 6. 执行模型优化
+opt.run()
+```
+
+转换完成后在output目录下会生成对应的hrnet_w18.nb文件。
+
+#### 3.3.2 更新模型
+将优化好的`.nb`文件，替换安卓程序中的 app/src/main/assets/image_matting/
+models/modnet下面的文件即可。
+
+然后在工程中修改图像输入尺寸：打开string.xml文件，修改示例如下：
+``` xml
+<string name="INPUT_SHAPE_DEFAULT">1,3,256,256</string>
+```
+1,3,256,256分别表示图像对应的batchsize、channel、height、width，我们一般修改height和width即可，这里的height和width需要和静态图导出时设置的尺寸一致。
+
+整个安卓demo采用java实现，没有内嵌C++代码，构建和执行比较简单。未来也可以将本demo移植到java web项目中实现web版人像抠图。

contrib/Matting/deploy/human_matting_android_demo/app/.gitignore

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+/build